Google Scholar & DBLP

Journal Articles

  1. S. H. Muggleton, W.-Z. Dai, C. Sammut, A. Tamaddoni-Nezhad, J. Wen, and Z.-H. Zhou. Meta-interpretive learning from noisy images. Machine Learning, Machine Learning, 2018, 107(7): 749-766.
  2. W.-Z. Dai and Z.-H. Zhou. A Survey on Inductive Logic Programming. Journal of Computer Research and Development, 2019, 56(1): 138-154. (In Chinese)

Conference Papers

  1. W.-Z. Dai, Q. Xu, Y. Yu, and Z.-H. Zhou. Bridging machine learning and logical reasoning by abductive learning. In: Advances in Neural Information Processing Systems 32 (NeurIPS’19) (Vancouver, Canada), 2019. (Codes / Slides / Poster)

  2. W.-Z. Dai, S. H. Muggleton, J. Wen, A. Tamaddoni-Nezhad, and Z.-H. Zhou. Logic vision: One-shot meta-intepretive learning from real images. In: Proceedings of the 25th International Conference on Inductive Logic Programming (ILP’17), Orleans, France, 2018, pp.46-62. (Codes)

  3. W.-Z. Dai and Z.-H. Zhou. Combining logic abduction and statistical induction: Discovering written primitives with human knowledge. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17), San Francisco, CA, 2017, pp.4392-4398. (Codes)

  4. W.-Z. Dai, S. H. Muggleton, and Z.-H. Zhou. Logic vision: Meta-intepretive learning for simple geometrical concepts. In: Late Breaking Papers of the 25th International Conference on Inductive Logic Programming (ILP’15), Kyoto, Japan, 2016. (Codes)

  5. W.-Z. Dai and Z.-H. Zhou. Statistical unfolded logic learning. In: Proceedings of the 7th Asian Conference on Machine Learning (ACML’15), Hong Kong, 2015, JMLR: W&CP 45, pp. 349-361.

Workshop Papers

  1. W.-Z. Dai, Y. Yu, and Z.-H. Zhou. Lifted-rollout for approximate policy iteration of Markov decision process. In: Proceedings of the 11th IEEE International Conference on Data Mining Workshops (International Workshop on Learning and Data Mining for Robotics (LEMIR’11), in conjunction with ICDM’11), Vancouver, Canada, 2011, pp.689-696.

Thesis